Slippage is the gap between the execution price a trading decision assumes and the price the order actually receives when it fills.

Slippage is the gap between the execution price a trading decision assumes and the price the order actually receives when it fills. In practice, a model may assume a trade occurs at a quoted price, a mid price, or a bar close. The realized fill can differ because markets move between decision time and execution time, because liquidity at the displayed price is limited, or because routing and venue selection expose the order to different prices across markets.

The mechanism is straightforward. A strategy generates an order at a specific time using an assumed reference price. The order then interacts with the market as it is routed and filled. If the market moves, if the quoted size is not sufficient, or if another venue is posting a different price at the same moment, the final execution can occur at a different level than the model assumed. That difference is slippage.

A simple way to express slippage is:

slippage = realized fill price minus assumed execution price

The sign convention depends on the use case. For a buy order, a higher realized fill than the assumed price is adverse slippage. For a sell order, a lower realized fill than the assumed price is adverse slippage. Many execution studies also translate the price difference into basis points relative to the assumed price so results can be compared across instruments.

Cross venue data is important because the assumed price and the achievable price depend on where liquidity is available at the relevant instant. Sonar describes its cross venue data as covering trades and quotes from multiple venues with synchronized timestamps that support venue level analysis of market activity and execution conditions. That structure is the key requirement for measuring slippage accurately because it lets an analyst line up the decision timestamp, the contemporaneous quotes across venues, and the actual fill event for the same order.

With cross venue trade and quote data, the measurement process is direct. First, identify the time the model generated the order and record the model's assumed execution price. Second, align that decision time to synchronized quote data across venues to establish the market state that was actually available. Third, attach the realized fill price and timestamp from the executed order. Fourth, compute the price difference and, if needed, aggregate it by venue, instrument, time of day, order type, or liquidity regime.

Using Sonar's cross venue data platform for this type of analysis follows the same logic. The platform's stated focus on cross venue trades, quotes, and synchronized timestamps provides the inputs needed to compare a model assumption with observed execution outcomes across markets. An analyst can use those records to test whether a backtest assumption such as filling at the primary quote, the consolidated best quote, or a bar close systematically differs from fills observed when the order is exposed to multiple venues.

This matters because execution assumptions are one of the ways simulated results can diverge from live trading conditions. Sonar's backtest overfitting audit tool emphasizes that research quality depends on checking whether reported outcomes rely on fragile assumptions. Slippage analysis complements that discipline by testing whether the assumed execution rule is realistic when compared with timestamped market data and actual fills.

The same caution appears in Sonar's glossary entry on the deflated Sharpe ratio, which explains that apparently strong backtest statistics can be overstated when they do not adequately account for uncertainty and multiple testing. Slippage is part of that broader measurement problem. If execution is modeled too optimistically, the inputs to any downstream performance statistic can be biased.

Covered in depth in the Cross-venue market data & signals pillar hub.